A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

Almakdi, Sultan; Almulihi, Ahmed; Alqahtani, Ali; Rajab, Khairan; Alqhatani, Abdulmajeed; Shaikh, Asadullah

doi:10.32604/iasc.2022.023753

Cited by 6 publications

(4 citation statements)

References 44 publications

(46 reference statements)

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“…In contrast, the proposed ensemble based model produced promising results. The comparison of different thresholding methods and state-of-the-art methods (DOLHGS [ 30 ], EDB-CNN [ 31 ], Auto-ED [ 32 ], LIN [ 33 ], FCN-8 s [ 34 ], and U-Net [ 35 ]) for skin lesion segmentation using same datasets are given in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods

et al. 2023

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In recent times, there has been a huge increase in the average number of cases of skin cancer per year, which sometimes become life threatening for humans. Early detection of various skin diseases through automated detection techniques plays a crucial role. However, the presence of numerous artefacts makes this task challenging. Dermoscopic images exhibit various variations, including hair artefacts, markers, and ill-defined boundaries. These artefacts make automatic analysis of skin lesion quite a difficult task. To address these issues, it is essential to have an accurate and efficient automated method which will delineate a skin lesion from the rest of the image. Unfortunately, due to the presence of several types of skin artefacts, there is no such thresholding method that can provide a sufficient segmentation result for every type of skin lesion. To overcome this limitation, an ensemble-based method is proposed that selects the optimal thresholding based on an objective function. A group of state-of-the-art different thresholding methods such as Otsu, Kapur, Harris hawk, and grey level are used. The proposed method obtained superior results (dice score = 0.89 with p-value ≤ 0.05) as compared to other state-of-the-art methods (Otsu = 0.79, Kapur = 0.80, Harris hawk = 0.60, grey level = 0.69, active contour model = 0.72). The experiments conducted in this study utilize the ISIC 2016 dataset, which is publicly available and specifically designed for skin-related research. Accurate segmentation will help in the early detection of many skin diseases.

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Section: Methodsmentioning

confidence: 99%

Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods

et al. 2023

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“…These models may learn to recognize common patterns and structures present in the data by training CNNs on massive datasets of CT images. By assuming missing information from the input scans, CNNs can produce high-quality reconstructions [108][109][110][111]. In comparison to conventional techniques, CNN-based computed tomography reconstruction models have demonstrated significant advancements [112], yielding reconstructions that exhibit higher precision and aesthetic appeal.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman,

Khan,

Sadiq

et al. 2023

Tomography

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Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

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“…Model examination depends on techniques that include examining behaviors and clustering files along with the same behavior which we can label as malware. Artificial intelligence can be utilized for malware identification with the help of deep learning [27][28][29]. These depend on various steps such as aspect extraction, and identification of neural network layers, and the results are examined to identify malware.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Forensics Analysis of IoT Nodes using Machine Learning

Arshad

Tariq

Riaz

et al. 2022

JCBI

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With the versatility and exponential growth of IoT solutions, the probability of being attacked has increased. Resource constraint IoT devices raised a challenge for the security handler to track logs of different variety of attacks generated on them while performing the forensic analysis. Commonly forensic analysis is performed on the devices that calculate how much loss has occurred to the device due to the diversity of attacks. The main objective of this paper to develop a framework through which secueity can perfrom the forensic analysis on resource contraint IoT devices. In this paper, we have proposed a framework that intelligently performs forensic analysis and detects the different types of attacks performed on the endpoint (IoT device) using a node to node (N2N) framework. Furthermore, this proposed solution is a blend of different forensic tools and Machine learning techniques to identify different types of attacks. Using a third-party log server, the problem of evidence recovery from the endpoint under attack is addressed. To determine the nature and effect of the attack we have used the logs by using the security onion (forensic server). Additionally, this framework is equipped to automatically detect attacks by using the different machine learning algorithms. The efficiency of machine learning models is measured upon the values of (1) Accuracy, (2) Precision, (3) Recall, and (4) F-Measure. The results show that the decision tree algorithm stands out with the optimum performance compared to other ML models. Overall this framework can be used for the secuirty of IoT devices as well as the evidence collection from the IoT endpoint. For the validation of the proposed framework more detailed results and performance, analysis is presented in this paper.

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A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

Cited by 6 publications

References 44 publications

Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods

Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Digital Forensics Analysis of IoT Nodes using Machine Learning

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